Designing Affordable Rural Community Microgrids

A final focus model for heavy-ion fusion driver system codes

Grid-level battery energy storage systems have been proposed as a technology that can mitigate the adverse effects of stochastic renewable energy sources by performing electric energy time shift on the grid. In this work, full-year hourly AC optimal power flow simulations are supplemented by a battery energy storage scheduling routine that utilises multi-period optimal power flow simulations, with dispatch costs based on battery degradation. Different penetration and cost scenarios for the 2030 central European transmission grid are investigated. It is found that increasing battery energy penetrations lead to a lower overall cost of electricity and significantly less wind and solar photovoltaic power curtailment, but that the capacity factors of battery energy storage systems also decrease considerably. The utilisation of battery energy storage systems is also highly dependent on geographic location, with systems located in Austria, southern and western Germany achieving relatively higher capacity factors.

This paper proposes a method for three-phase state estimation (SE) in power distribution network including onload tap changers (OLTC) for voltage control. The OLTC tap positions are essentially discrete variables from the SE point of view. Estimation of these variables in SE presents a formidable challenge. The proposed methodology combines discrete and continuous state variables (voltage magnitudes, angles, and tap positions). A hybrid particle swarm optimization (HPSO) is applied to obtain the solution. The method is tested on standard IEEE 13- and 123-bus unbalanced test system models. The proposed algorithm accurately estimates the network bus voltage magnitudes and angles, and discrete tap values. The HPSO-based tap estimation provides a more accurate estimation of losses in the network, which helps in fair allocation of cost of losses in arriving at overall cost of electricity.

This article aims to develop a smart isolated energy community (EC) by coupling the neighbouring rural community microgrids (CMGs) with enhanced droop control for efficient power sharing. This recommended solution employs a power management (PM) based droop-control to enable independent neighbouring CMGs to share power on an available basis by not constraining CMG inverters to equal power sharing. During the grid-connected mode, the droop control may have different power setpoints of each CMG. However, during the standalone mode of operation, the power setpoint should be defined according to their power rating and availability to maintain the system stability. In this article, a PM strategy is developed to maintain the power setpoints of the autonomous CMGs. An improper selection of power setpoints in autonomous CMG can raise the DC link voltage to an unmanageable value and can cause an inadvertent shutdown of CMG. The suggested PM-based droop control enables the CMG inverter not to restrict the inverter to equal power share but to distribute its active power as available in an asymmetric way, if required. The dynamic performance of the proposed coupled system incorporated with two remote isolated CMGs is investigated in a MATLAB environment. Further, a laboratory prototype of the proposed system has been developed using a LabVIEW-based sbRIO controller to verify the efficacy of the proposed approach.

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Poor access to electricity in rural communities has been linked to a poor educational system, as electricity is essential for supporting laboratories, technical practice, and long study hours for students. Therefore, this work presents the techno-economic analysis of a hybrid solar PV–agro-wastes (syngas) energy system for electricity, heat, and cooling generation to improve energy access in rural schools. The system is located in Ghana at Tuna (lat. 9°29′18.28″ N and long. 2°25′51.02″ W) and serves a secondary school for enhanced quality education. The system relies on agro-waste (gasifier-generator) and sunlight (solar PV), with a battery energy storage system, to meet the school’s energy demand. The study employs HOMER Pro Version 3.16.2 software to comprehensively analyze technical, economic, and environmental aspects. The system can generate 221,621 kWh of electricity (at a unit cost of electricity of 0.295 EUR/kWh) and 110,896 kWh of thermal energy yearly. The cost of electricity from the proposed system is cheaper than the cost of electricity from an equivalent diesel generator at 0.380 EUR/kWh. The thermal energy can meet the heating demand of the school in addition to powering a vapor absorption chiller. The system is environmentally friendly, with the capacity to sink 0.526 kg of CO2 yearly. Government policies that moderate interest rates for bioenergy/solar PV systems and social solution on feedstock pricing will favor the economic sustainability of the proposed system. The system will address the energy access challenge (SDG 7), enhance the quality of education (SDG 4), and contribute to climate mitigation through carbon sequestration (SDG 13).

As the proportion of renewable energy power generation continues to increase, the number of grid-connected microgrids is gradually increasing, and geographically adjacent microgrids can be interconnected to form a Micro-Grid Community (MGC). In order to reduce the operation and maintenance costs of a single micro grid and reduce the adverse effects caused by unnecessary energy interaction between the micro grid and the main grid while improving the overall economic benefits of the micro grid community, this paper proposes a bi-level energy management model with the optimization goal of maximizing the social welfare of the micro grid community and minimizing the total electricity cost of a single micro grid. The lower-level model optimizes the output of each equipment unit in the system and the exchange power between the system and the external grid with the goal of minimizing the operating cost of each microgrid. The upper-level model optimizes the goal of maximizing the social welfare of the microgrid. Taking a microgrid community with four microgrids as an example, the simulation analysis shows that the proposed optimization model is beneficial to reduce the operating cost of a single microgrid, improve the overall revenue of the microgrid community, and reduce the power interaction pressure on the main grid.

78.2 Impact of Climate Change on the Health and Well-Being of Indigenous Communities in Australia

Water supply schemes should be technically and socially sustainable for smooth running, smooth operation and maintenances. Failure in either one of these leads to the whole system failure. Once the system is technically feasible, we can manage its social aspects. In this paper, we deal with technical sustainability of corrosive water, its impacts and the remedial measures. Corrosive water can severely damage intakes, metal pipes, cemented chambers, concrete reservoir and household plumbing systems. Hence, it is necessary to identify the corrosiveness of the water and should be neutralized. There are several solutions to this problem, however, they are not sustainable and economical for semi urban and rural communities. In this paper, we propose a sustainable, natural and economical solution to determine the corrosiveness of water and to effective technology to neutralize it. The proposed solution uses naturally available resources, hence makes it sustainable and affordable. Once the proposed method is deployed, it doesn’t require additional resources such as continuous monitoring, skilled manpower and electrical power cost etc. In this method, initially, the corrosiveness of the water is identified using parameters such as: total dissolved solid (TDS), pH, temperature, calcium and alkalinity. The corrosive water is then analyzed in laboratory. With carbon dioxide kinetic study, we verify the design of stabilization tank volume calculation. The research outcome is applied in the field to upgrade intake, pipe and fitting designs. The experiment in this research is conducted with the data obtained from several water supply schemes including Padampokhari water supply scheme in Nepal and tested in the same water supply scheme.

Existing work in energy demand side management focuses on the interaction between the utility grid and consumers. However, the previous technique is not focused on energy trading in local community of a renewable energy generation, distributed demand side management and not suitable for real-time environment. This paper presents a distributed demand side management system among multiple homes in community microgrid, with the integration of the internet of things smart meter and in the presence of renewable energy sources. The proposed energy consumption game is formulated for minimizing the cost of electricity in the individual home and the total cost of energy consumption in the whole community. The smart home users are playing game by optimizing their own daily energy consumption of appliances. The multiple participants include the self renewable generation of users, shared community microgrid and optional utility company. Each participant applies its best strategy to minimize energy consumption cost and users can maintain their own privacy of energy consumption. Moreover, the proposed scheme is distributed on blockchain, which provides a trusted communication medium between the participants. It enforces the autonomous monitoring of smart appliances and the billing of electricity consumption via smart contracts. Solidity smart contract is deployed to facilitate the execution of transactions without the involvement of third party in the smart community. Comparison of the results show that the proposed approach minimizes the total cost of energy consumption as well as each user's energy consumption cost.

Remote locations often face an acute power shortage due to poor connectivity with the grid or intermittent power supply from the utility. To mitigate this challenge, this paper proposes a multi-energy system-based community microgrid, which consists of various electrical and thermal generation units, thereby allowing localized generation-load matching. In particular, the selected community microgrid system consists distributed energy resources such as solar PV system, battery energy storage system, and a biomass gasifier for power production. On the other hand, high power-consuming loads such as cold storage, a flour mill, critical, and domestic loads within a village are also modeled and included, through actual surveying in a rural location in India. Further, using these demand and generation inputs, the optimal dispatch schedule for the proposed multi-energy system is obtained by solving a mixed integer non-linear programming problem. It is shown that the proposed multi-energy system can meet the demand of the village cost-effectively, subject to the operational constraints of all appliances and generators. Additionally, based on the economic analysis of the system, this paper also develops a self-sustainability plan for the microgrid operation for the socio-economic welfare of the community.

Photovoltaic hybrid power generation is an alternative solution for supplying electricity to rural areas, especially in Sarawak, Malaysia, where grid connection is almost impossible due to the cost and geographic location. Diesel generator systems are still used by local communities in these remote areas, and the implementation of photovoltaic diesel hybrid systems can reduce dependency on diesel generator units. The work presented in this paper explores the possibility of integrating solar energy resource with diesel generator to meet the load demand of rural communities in Sarawak. In further detail, the configuration and size of the PV-diesel system are analyzed in based on the lowest net present cost (NPC) and the cost of electricity (COE). The analysis is based on mathematical modeling and simulation using Hybrid Optimization Model for Electric Renewables (HOMER) software. Eight different configurations of two energy resources-photovoltaic panels and diesel generators-are studied and compared. The option with the highest optimization values is considered to be the most feasible electrification solution for the particular rural area. The outcomes of the analysis show that the PV-diesel hybrid configuration proves to be more cost-effective compared to an existing generator-based system in that it reduces fuel dependency and has the lowest NPC and COE among all configurations studied.

Smart Grid (SG) plays a noteworthy role in minimizing the Electricity Cost (EC) through Demand Side Management (DSM). Smart homes are the part of SG, pays a lot in minimizing EC via scheduling the appliances. Home Energy Management (HEM) have been extensively used for energy management in smart homes. In this paper, for the effective utilization of energy in a smart home, we propose a solution that consists of bio-inspired techniques: Genetic Algorithm (GA), Flower Pollination Algorithm (FPA) and hybrid of these two, Genetic Flower Pollination Algorithm (GFPA). All of these techniques applied to the appliances that are essential in a home. Our proposed solution leads to find an optimal scheduling pattern that reduces EC, Peak to Average Ratio (PAR) and maximize User Comfort (UC). In our work, we have considered one home. We divide appliances into three categories, non-interruptible, interruptible and fixed appliances. Simulation results show that our proposed schemes performed better in terms of EC, UC and PAR. We have done this work for three different Operational Time Intervals (OTIs) 15, 30 and 60 minutes for each appliance.

The control scheme is key for providing a stable and reliable grid operation under best economical conditions primarily in rural microgrids. The aim of this study was to investigate the use of a blackstart control scheme considering small production processes and its potential benefits for the whole system. A microgrid (MG) model based on the practical features of an actual MG called Huatacondo, which is located in the north of Chile has been developed to asses the control scheme capabilities. The results show that using the blackstart control scheme combined with the knowledge of the load behavior of the processes, it is feasible to maintain the system within its normal operating conditions. This indicates that when a failure in the system occurs, the control scheme is capable of recovering the whole system avoiding a severe decline in the frequency. Thus, the system can be operated in a cost-efficient way.

Hybrid renewable distributed generation systems are a potential solution to both South Africa's high greenhouse gas emissions and aspirations for universal electrification for remote rural communities. Indigenous resources such biomass, solar and wind are widely available in the country which can preferably be harnessed to supply energy to these communities by implementing hybrid renewable energy systems. For implementing such projects, economic feasibility studies are necessary to compare various scenarios of hybridization in terms of cost parameters such as Net Present Cost (NPC) and Cost of Electricity (COE). This paper presents the economic feasibility analyses of biogas, solar and wind-based stand-alone and grid-connected HRES for dairy farm-based energy generation. These systems are designed to not only cater for the energy needs of the farm but also for the local community. The possibility of grid-sales is also studied for grid-connected systems. Based on the findings, recommendations are made to include principles on financing and regulation to ensure that these distributed systems are more economically viable and more widely adopted.